plus sc10x
load dependancies
load 10x data
GEX.seur <- readRDS("./sc10x_LYN.marker.sort0829.rds")
GEX.seur## An object of class Seurat
## 17590 features across 24766 samples within 1 assay
## Active assay: RNA (17590 features, 1199 variable features)
## 3 dimensional reductions calculated: pca, tsne, umapcolor.FB <- ggsci::pal_igv("default")(49)[c(8,33,40,
34,26,1,28,
2,43,18)]
color.cnt <- color.FB[c(3,2,1,
10,9,8,
7,6,4)]DimPlot(GEX.seur, reduction = "umap", label = F, group.by = "preAnno") +
DimPlot(GEX.seur, reduction = "umap", label = F, group.by = "cnt", cols = color.cnt)
VlnPlot(GEX.seur, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0, group.by = "sort_clusters")
cleaning up
keep only Microglia clusters
remove DF0.05 doublets
remove AD.GFP.2
GEX.seur <- subset(GEX.seur, subset = preAnno %in% c("Microglia") &
DoubletFinder0.05=="Singlet" &
FB.info %in% setdiff(levels(FB.info),"AD.GFP.2"))
GEX.seur <- subset(GEX.seur, subset = percent.mt < 8 & nFeature_RNA < 2800 & nCount_RNA < 8500)
GEX.seur## An object of class Seurat
## 17590 features across 21021 samples within 1 assay
## Active assay: RNA (17590 features, 1199 variable features)
## 3 dimensional reductions calculated: pca, tsne, umapDimPlot(GEX.seur, reduction = "umap", label = F, group.by = "preAnno") +
DimPlot(GEX.seur, reduction = "umap", label = F, group.by = "cnt", cols = color.cnt)
DimPlot(GEX.seur, reduction = "umap", label = F, group.by = "seurat_clusters") +
DimPlot(GEX.seur, reduction = "umap", label = F, group.by = "FB.info", cols = color.FB[c(1,2,3,4,6,7,8,9,10)])
VlnPlot(GEX.seur, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0.1, group.by = "sort_clusters")
VlnPlot(GEX.seur, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0, group.by = "sort_clusters")
VlnPlot(GEX.seur, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0.1, group.by = "cnt", cols = color.cnt)
VlnPlot(GEX.seur, features = c("nFeature_RNA", "nCount_RNA", "percent.mt","percent.rb"), ncol = 2, pt.size = 0, group.by = "cnt", cols = color.cnt)
plota <- FeatureScatter(GEX.seur, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "cnt", cols = color.cnt)
plotb <- FeatureScatter(GEX.seur, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "cnt", cols = color.cnt)
plotc <- FeatureScatter(GEX.seur, feature1 = "nCount_RNA", feature2 = "percent.rb", group.by = "cnt", cols = color.cnt)
plota + plotb + plotc
par(mar=c(6,4,2,3))
sl_stat <- table(GEX.seur$cnt)
barplot(sl_stat,ylim = c(0,3800),
#col = c("#FF6C91","lightgrey",color.FB),
col = c(color.cnt),
main = "Feature Barcode statistics",cex.names = 0.75, xaxt = "n")
axis(1,1:9*1.2-0.48 ,levels(GEX.seur@meta.data$cnt), las=3, cex.axis=0.85)
text(x=1:9*1.2-0.45,y=sl_stat+245,paste0(sl_stat,"\n",100*round(as.numeric(sl_stat/sum(sl_stat)),4),"%"),cex = 0.75)
new
re-clustering
GEX.seur <- FindVariableFeatures(GEX.seur, selection.method = "vst", nfeatures = 1800)
# Identify the 10 most highly variable genes
#top10 <- head(VariableFeatures(GEX.seur), 10)
top20 <- head(VariableFeatures(GEX.seur), 20)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(GEX.seur)
plot2 <- LabelPoints(plot = plot1, points = top20, repel = T)
plot1 + plot2
head(VariableFeatures(GEX.seur), 300)## [1] "Cxcl10" "Spp1" "Ccl4" "Cxcl13"
## [5] "Ccl5" "Ccl3" "Cxcl9" "Ccl12"
## [9] "Spp1-EGFP" "Mmp12" "Cd74" "H2-Aa"
## [13] "Gpnmb" "Ccl2" "Rsad2" "Cst7"
## [17] "Cck" "Top2a" "H2-Ab1" "Lpl"
## [21] "Ttr" "Lgals3" "Mki67" "Fabp5"
## [25] "Iigp1" "Ifi27l2a" "Fn1" "H2-Eb1"
## [29] "Apoc1" "Ccl6" "Gdf15" "Saa3"
## [33] "Egr1" "Ifitm3" "Apoe" "Enpp2"
## [37] "Wfdc17" "Ifit3" "Ch25h" "Il1b"
## [41] "Prc1" "Il1rn" "Lyz2" "Hist1h1b"
## [45] "Tnf" "Ifit1" "Ccl7" "Stmn1"
## [49] "Pclaf" "Cxcl2" "Egr3" "Ifit2"
## [53] "Igf1" "Cenpf" "Nusap1" "Ube2c"
## [57] "Gm1673" "Dqx1" "Cd5l" "Aldh1a3"
## [61] "Igfbp5" "Ank" "Cdkn1a" "Ly6k"
## [65] "Apoc4" "Gm43409" "Isg15" "Slc7a11"
## [69] "Wdcp" "Ms4a7" "Clec4d" "Hmmr"
## [73] "Itgax" "Prdx1" "Cox6a2" "Cd52"
## [77] "Ccl8" "Csf1" "Birc5" "Atf3"
## [81] "Ctla2a" "Ifnb1" "Ctsd" "Cd72"
## [85] "Syngr1" "Cybb" "Gbp2" "Gnas"
## [89] "Vim" "Ctsb" "Bst2" "Ly6a"
## [93] "Postn" "Olfr1369-ps1" "Lilrb4a" "Tyrobp"
## [97] "B230312C02Rik" "Mif" "Cd69" "Neil3"
## [101] "Gm45184" "Aplp2" "Plp1" "C4b"
## [105] "Dennd5b" "Gm26885" "Nr4a1" "Fth1"
## [109] "Edn1" "Ccnb2" "Lgals1" "Ifit3b"
## [113] "Ftl1" "F830016B08Rik" "Cd63" "Olr1"
## [117] "Ramp1" "Pbld1" "Lgals3bp" "Chac1"
## [121] "Cdca8" "Bex1" "Pbk" "Cenpa"
## [125] "Slfn5" "Trib3" "Ifi204" "Clu"
## [129] "Hmgb2" "Knl1" "Hif1a" "Ccr1"
## [133] "Hist1h3c" "Flt1" "Ifi207" "Serpine2"
## [137] "Mamdc2" "Gadd45b" "Ctsl" "Ifi44"
## [141] "Pou3f1" "Gria2" "Sprr1a" "Oasl1"
## [145] "Ccl9" "Colec12" "Ctsz" "Ifi211"
## [149] "Tcap" "Kif11" "Id3" "Fcgr4"
## [153] "Ifitm1" "Ccdc63" "Ms4a4b" "Id2"
## [157] "Esco2" "Dkk2" "B930036N10Rik" "Il4i1"
## [161] "Gdf3" "Grb14" "Gpr85" "Rgs1"
## [165] "Irf7" "Tpx2" "Rrm2" "Ptgds"
## [169] "Fxyd5" "Enah" "Cstb" "Gm26737"
## [173] "Lgi2" "Lockd" "Phlda3" "Trem2"
## [177] "Cxcl14" "Cd9" "Acod1" "Baiap2l2"
## [181] "Hist1h2ap" "Gm34455" "Crip1" "Ecrg4"
## [185] "Cenpe" "Mt1" "Oasl2" "Timp2"
## [189] "Ccl22" "Clec12a" "Egfl7" "Rhox5"
## [193] "Spag5" "H2-Q7" "Gapdh" "Loxl2"
## [197] "mt-Co1" "Slamf7" "Tspo" "Gm21738"
## [201] "Gm48837" "Uchl1" "Ccna2" "Stap1"
## [205] "Ptprf" "Ppp1r14a" "Alpk2" "Crlf2"
## [209] "Plac8" "Aspm" "Gm8251" "Rab3c"
## [213] "Ccnb1" "Hbegf" "Capg" "Kif23"
## [217] "Gfod2" "Rpl41" "Usp18" "Snca"
## [221] "Ifit1bl1" "Sgo2a" "Gm10800" "Mgl2"
## [225] "Shcbp1" "Gpr84" "Pkm" "Hp"
## [229] "2810403D21Rik" "Gm49756" "Cdc20" "Ifi208"
## [233] "Kcnk9" "Pld3" "Fabp3" "Plk2"
## [237] "Cspg4" "Nes" "Spc24" "Cd40"
## [241] "Cd83" "Kcnq1ot1" "Gm10457" "A330032B11Rik"
## [245] "Scd2" "Ankle1" "Gm4951" "Slc7a14"
## [249] "Tlr2" "Anln" "Tnip3" "Rcan1"
## [253] "Ms4a4c" "Tk1" "Slfn2" "Ptgs2"
## [257] "Gm17251" "Tbx19" "Meis2" "Lrrc31"
## [261] "Sh3d19" "Rgs17" "Olfr889" "Stat1"
## [265] "Peg3" "Tnfrsf11b" "Ptprz1" "Axl"
## [269] "Zbp1" "Madcam1" "Lhfpl4" "Apod"
## [273] "H2-D1" "Adamts1" "Mal" "Plau"
## [277] "Pf4" "Atp6v0d2" "Cd68" "Syt5"
## [281] "Selenow" "Gm4841" "H2-K1" "1700120C14Rik"
## [285] "Gm29773" "Slc1a2" "Gm14636" "Fam20c"
## [289] "Serpina3g" "Cd36" "Bub1" "Cit"
## [293] "Iqgap1" "Aurka" "Smc2" "Pcgf2"
## [297] "Ptgs2os2" "Smtnl1" "Mir670hg" "Gchfr"# exclude MT genes and more
# add sex-related Xist/Tsix
DIG <- grep("^Tra|^Trb|^Trg|^Trd|^Tcr|^Igm|^Igh|^Igk|^Igl|Jchain|Mzb1|Vpreb|Lars2|Jun|Fos|Egr1|^Hsp|^Rps|^Rpl|Hbb-|Hba-|^Dnaj|^AC|^AI|^AA|^AW|^AY|^BC|^Gm|^Hist|Rik$|-ps|Xist|Tsix|^Ifi|^Isg|^Mcm",
rownames(GEX.seur),value = T)
CC_gene <- Hmisc::capitalize(tolower(as.vector(unlist(cc.genes.updated.2019))))
MT_gene <- grep("^mt-",rownames(GEX.seur),value = T)
VariableFeatures(GEX.seur) <- setdiff(VariableFeatures(object = GEX.seur),
c(MT_gene,
DIG,
CC_gene) )
GEX.seur <- RunPCA(GEX.seur, features = VariableFeatures(GEX.seur), seed.use = 868)## PC_ 1
## Positive: Apoe, Cst7, Fth1, Fabp5, Ctsb, Cd63, Lyz2, Ctsd, Ctsz, Cd52
## Igf1, Lpl, Tyrobp, Ftl1, B2m, Mif, Gnas, Npc2, Cd9, Ank
## Csf1, Lgals3bp, Ccl3, Itgax, Hif1a, Eef1a1, Ch25h, Lilrb4a, H2-D1, Ctsl
## Negative: Crybb1, Ecscr, Hpgd, Slc2a5, Lrba, Camk2n1, Filip1l, Stab1, Slc40a1, Il7r
## Sox4, Lst1, Ddit4, Bank1, Rtn1, Tent5a, Fam102b, Itga9, Serpinf1, Btg2
## Fcrls, Lifr, Gp9, Plxna4, Klf7, Upk1b, Cdk5r1, Nav3, Gbp7, Inpp4b
## PC_ 2
## Positive: Ccl12, Iigp1, Ccl2, Irf7, Fcgr4, C4b, Zbp1, Slfn5, Fgl2, Rsad2
## Stat1, Slfn2, Gbp2, H2-Q7, Oasl2, Oasl1, H2-K1, Cd72, Bst2, Cxcl10
## Rtp4, Tspo, Usp18, Ly6a, Phf11b, H2-D1, Rnf213, Parp14, Ms4a4c, Tor3a
## Negative: Trem2, Ctsd, Ank, Tyrobp, Igf1, Ramp1, Serpine2, Pmp22, Ccl6, Hpgd
## Mamdc2, Dkk2, Baiap2l2, Hexa, Ang, Cd68, Fabp5, Itgax, Gpnmb, Syngr1
## Aplp2, Cadm1, Scd2, Fcrls, Cst7, Cd9, Creg1, Sox4, Arap2, St8sia6
## PC_ 3
## Positive: Stmn1, Pclaf, Spc24, Prc1, Pbk, Tk1, Fcrls, Knl1, Esco2, Lockd
## Ccna2, Smc2, Ezh2, Actg1, Ccl4, Spc25, Asf1b, Ccnf, Ran, Racgap1
## Calr, Fam102b, Ncl, Lmnb1, Mis18bp1, Ncapg2, Tagln2, Kif15, Slc15a3, Tubb5
## Negative: Apoe, Fau, H2-D1, B2m, Lyz2, H2-K1, Fth1, Eef1a1, Eef1b2, H2-Q7
## Zfas1, Gas5, Timp2, Uqcrh, Cd52, Rack1, Cd74, Tyrobp, Trem2, Npc2
## Uba52, Ctsl, Cd63, Apoc1, C4b, Lgals3bp, Ccl5, H2-Ab1, Ly86, Crlf2
## PC_ 4
## Positive: Serpine2, Grn, Tyrobp, Ramp1, Hexa, Oasl2, Syngr1, Arsb, Trem2, Ctsd
## Rtp4, Baiap2l2, Irf7, Gnas, Ly86, Rsad2, Fkbp2, Slfn5, Bst2, Trim30a
## Cst7, Slc2a5, Oasl1, Herc6, Ang, Usp18, Psat1, Xaf1, Crybb1, Fam102b
## Negative: Lgals3, Lilrb4a, Atf3, Id2, Ccl4, Lilr4b, Fn1, Vim, Apoe, Iqgap1
## Mmp12, Bcl2a1d, Cybb, Spp1, Fth1, Glipr1, Plaur, Lpl, Plk2, Emp3
## Spp1-EGFP, Rai14, Cd44, C3, Cxcl16, Slamf7, Nes, Tnfrsf12a, Fam20c, Cd74
## PC_ 5
## Positive: Pclaf, Prc1, Spc24, Stmn1, Pbk, Knl1, Fau, Esco2, Lockd, Tk1
## Ccna2, Racgap1, Mis18bp1, Ccnf, Fth1, Spc25, Smc2, Ezh2, Trim59, Kif15
## Asf1b, Knstrn, Lig1, Dut, Ncapg2, H2afx, Cit, Cenpk, H2-D1, Diaph3
## Negative: Rsad2, Slfn5, Usp18, Oasl1, Parp14, Herc6, Oasl2, Irf7, Ccl4, Cxcl10
## Slc15a3, Fcrls, Slfn8, Rtp4, Trim30a, Cmpk2, Rnf213, Ccl3, Phf11d, Tnfsf10
## Znfx1, Stat2, Ddx58, Atf3, Lilrb4a, C3ar1, Cdk6, Iigp1, Irgm1, Xaf1length(VariableFeatures(GEX.seur))## [1] 1390head(VariableFeatures(GEX.seur),300)## [1] "Cxcl10" "Spp1" "Ccl4" "Cxcl13" "Ccl5" "Ccl3"
## [7] "Cxcl9" "Ccl12" "Spp1-EGFP" "Mmp12" "Cd74" "H2-Aa"
## [13] "Gpnmb" "Ccl2" "Rsad2" "Cst7" "Cck" "H2-Ab1"
## [19] "Lpl" "Ttr" "Lgals3" "Fabp5" "Iigp1" "Fn1"
## [25] "H2-Eb1" "Apoc1" "Ccl6" "Gdf15" "Saa3" "Apoe"
## [31] "Enpp2" "Wfdc17" "Ch25h" "Il1b" "Prc1" "Il1rn"
## [37] "Lyz2" "Tnf" "Ccl7" "Stmn1" "Pclaf" "Cxcl2"
## [43] "Egr3" "Igf1" "Dqx1" "Cd5l" "Aldh1a3" "Igfbp5"
## [49] "Ank" "Cdkn1a" "Ly6k" "Apoc4" "Slc7a11" "Wdcp"
## [55] "Ms4a7" "Clec4d" "Itgax" "Prdx1" "Cox6a2" "Cd52"
## [61] "Ccl8" "Csf1" "Atf3" "Ctla2a" "Ifnb1" "Ctsd"
## [67] "Cd72" "Syngr1" "Cybb" "Gbp2" "Gnas" "Vim"
## [73] "Ctsb" "Bst2" "Ly6a" "Postn" "Lilrb4a" "Tyrobp"
## [79] "Mif" "Cd69" "Neil3" "Aplp2" "Plp1" "C4b"
## [85] "Dennd5b" "Nr4a1" "Fth1" "Edn1" "Lgals1" "Ftl1"
## [91] "Cd63" "Olr1" "Ramp1" "Pbld1" "Lgals3bp" "Chac1"
## [97] "Bex1" "Pbk" "Slfn5" "Trib3" "Clu" "Knl1"
## [103] "Hif1a" "Ccr1" "Flt1" "Serpine2" "Mamdc2" "Gadd45b"
## [109] "Ctsl" "Pou3f1" "Gria2" "Sprr1a" "Oasl1" "Ccl9"
## [115] "Colec12" "Ctsz" "Tcap" "Id3" "Fcgr4" "Ccdc63"
## [121] "Ms4a4b" "Id2" "Esco2" "Dkk2" "Il4i1" "Gdf3"
## [127] "Grb14" "Gpr85" "Rgs1" "Irf7" "Ptgds" "Fxyd5"
## [133] "Enah" "Cstb" "Lgi2" "Lockd" "Phlda3" "Trem2"
## [139] "Cxcl14" "Cd9" "Acod1" "Baiap2l2" "Crip1" "Ecrg4"
## [145] "Mt1" "Oasl2" "Timp2" "Ccl22" "Clec12a" "Egfl7"
## [151] "Rhox5" "Spag5" "H2-Q7" "Gapdh" "Loxl2" "Slamf7"
## [157] "Tspo" "Uchl1" "Ccna2" "Stap1" "Ptprf" "Ppp1r14a"
## [163] "Alpk2" "Crlf2" "Plac8" "Aspm" "Rab3c" "Ccnb1"
## [169] "Hbegf" "Capg" "Gfod2" "Usp18" "Snca" "Sgo2a"
## [175] "Mgl2" "Shcbp1" "Gpr84" "Pkm" "Hp" "Kcnk9"
## [181] "Pld3" "Fabp3" "Plk2" "Cspg4" "Nes" "Spc24"
## [187] "Cd40" "Cd83" "Kcnq1ot1" "Scd2" "Ankle1" "Slc7a14"
## [193] "Tlr2" "Tnip3" "Rcan1" "Ms4a4c" "Tk1" "Slfn2"
## [199] "Ptgs2" "Tbx19" "Meis2" "Lrrc31" "Sh3d19" "Rgs17"
## [205] "Olfr889" "Stat1" "Peg3" "Tnfrsf11b" "Ptprz1" "Axl"
## [211] "Zbp1" "Madcam1" "Lhfpl4" "Apod" "H2-D1" "Adamts1"
## [217] "Mal" "Plau" "Pf4" "Atp6v0d2" "Cd68" "Syt5"
## [223] "Selenow" "H2-K1" "Slc1a2" "Fam20c" "Serpina3g" "Cd36"
## [229] "Cit" "Iqgap1" "Smc2" "Pcgf2" "Ptgs2os2" "Smtnl1"
## [235] "Mir670hg" "Gchfr" "Cldn11" "Sh3gl2" "Elavl2" "Osbp2"
## [241] "Sdk2" "Fzd3" "Gpc4" "Amot" "Kif28" "Pdpn"
## [247] "Pnmal2" "Gabrb3" "Olfr56" "Igsf11" "Arx" "Fgl2"
## [253] "Cxcl16" "Npc2" "Naaa" "Sag" "Lrpap1" "Cd38"
## [259] "Anks1b" "Serpinb8" "Ccne1" "Phf11b" "Cpd" "Apoc2"
## [265] "Bcl2a1d" "C2" "Gfap" "Tpi1" "Grn" "Anxa5"
## [271] "Cp" "Ptprd" "Plaur" "Rtp4" "Gusb" "Aspa"
## [277] "Egr2" "Nfasc" "Cd3d" "Btnl4" "Kcnq3" "Crybb1"
## [283] "Myo1e" "Lat" "Ctnna3" "Syt1" "H2afz" "Bhlhe40"
## [289] "Prr11" "Parp14" "Cadm1" "Ier3" "Mxd3" "Hexa"
## [295] "Hpse" "St8sia6" "Ldha" "Slfn1" "Gbp4" "Lilr4b"DimPlot(GEX.seur, reduction = "pca",dims = 1:2, group.by = "orig.ident") +
DimPlot(GEX.seur, reduction = "pca",dims = 3:4, group.by = "orig.ident")
DimPlot(GEX.seur, reduction = "pca",dims = 1:2, group.by = "cnt", cols = color.cnt, ) +
DimPlot(GEX.seur, reduction = "pca",dims = 3:4, group.by = "cnt", cols = color.cnt)
#GEX.seur@reductions$pca@cell.embeddingscheck PC_1/2 median/mean
GEX.seur@meta.data[,c("PC_1","PC_2")] <- GEX.seur@reductions$pca@cell.embeddings[rownames(GEX.seur@meta.data),c("PC_1","PC_2")]cowplot::plot_grid(
as.data.frame(GEX.seur@meta.data) %>% select(cnt,PC_1,PC_2) %>%
group_by(cnt) %>%
summarize(m1=mean(PC_1), m2=mean(PC_2),
n1=median(PC_1), n2=median(PC_2)) %>%
ggplot(aes(x=m1, y=m2, color=cnt, label=cnt)) + geom_point(color=color.cnt) + geom_text_repel(show.legend = F,color=color.cnt, size=3.5) + labs(title="PC Mean") + theme_classic2(),
as.data.frame(GEX.seur@meta.data) %>% select(cnt,PC_1,PC_2) %>%
group_by(cnt) %>%
summarize(m1=mean(PC_1), m2=mean(PC_2),
n1=median(PC_1), n2=median(PC_2)) %>%
ggplot(aes(x=n1, y=n2, color=cnt, label=cnt)) + geom_point(color=color.cnt) + geom_text_repel(show.legend = F,color=color.cnt, size=3.5) + labs(title="PC Median") + theme_classic2(),
ncol = 2)
check PCA
cowplot::plot_grid(
FeaturePlot(GEX.seur, features = "Spp1", reduction = "pca",dims = 1:2),
DimPlot(GEX.seur, reduction = "pca",dims = 1:2, group.by = "cnt", cols = color.cnt),
rel_widths = c(5,5.6),
ncol = 2)
cowplot::plot_grid(
FeaturePlot(GEX.seur, features = "Spp1-EGFP", reduction = "pca",dims = 1:2),
DimPlot(GEX.seur, reduction = "pca",dims = 1:2, group.by = "cnt", cols = color.cnt),
rel_widths = c(5,5.6),
ncol = 2)
cowplot::plot_grid(
FeaturePlot(GEX.seur, features = "P2ry12", reduction = "pca",dims = 1:2),
DimPlot(GEX.seur, reduction = "pca",dims = 1:2, group.by = "cnt", cols = color.cnt),
rel_widths = c(5,5.6),
ncol = 2)
cowplot::plot_grid(
FeaturePlot(GEX.seur, features = "Mki67", reduction = "pca",dims = 1:2),
DimPlot(GEX.seur, reduction = "pca",dims = 1:2, group.by = "cnt", cols = color.cnt),
rel_widths = c(5,5.6),
ncol = 2)
DimPlot(GEX.seur, reduction = "pca",dims = 1:2, group.by = "cnt", split.by = "cnt", cols = color.cnt, ncol = 3)
DimHeatmap(GEX.seur, dims = 1:12, cells = 6000, balanced = TRUE,ncol = 4,nfeatures = 80)
(GEX.seur@reductions$pca@feature.loadings %>% as.data.frame)[,1:8] %>% arrange(desc(PC_1) ) %>% head(40)## PC_1 PC_2 PC_3 PC_4 PC_5
## Apoe 0.12500859 0.021924692 -0.1441137617 -0.0905009225 0.049730734
## Cst7 0.12238507 -0.048085902 -0.0358791868 0.0679274316 0.007009488
## Fth1 0.11034388 0.042927671 -0.0925807309 -0.0789392314 0.088070464
## Fabp5 0.11016458 -0.052799666 -0.0047849246 -0.0171977019 -0.021476707
## Ctsb 0.10990714 -0.008559184 -0.0281109256 0.0111130622 -0.023351234
## Cd63 0.10818542 0.009186587 -0.0488474404 -0.0351982363 0.027076546
## Lyz2 0.10678830 -0.006881866 -0.1042972513 -0.0183104502 0.054388518
## Ctsd 0.10574859 -0.089161487 -0.0219451625 0.0817485281 -0.030483656
## Ctsz 0.10441218 -0.012486622 -0.0276072630 0.0099890858 0.002926231
## Cd52 0.10227758 0.056533646 -0.0589972148 -0.0063074874 0.065536423
## Igf1 0.10020421 -0.070846684 0.0085575795 -0.0066821365 -0.039675793
## Lpl 0.09915171 -0.041189055 0.0528122116 -0.0752158158 -0.027318480
## Tyrobp 0.09599701 -0.074658856 -0.0572709372 0.0964621518 0.040726996
## Ftl1 0.09472093 -0.014117507 -0.0360902957 -0.0496492969 0.038236325
## B2m 0.09174523 0.078179665 -0.1107401923 0.0286066708 0.066697121
## Mif 0.09034305 -0.012032564 0.0244762234 0.0345747227 0.004733014
## Gnas 0.08854069 -0.029015510 0.0140652656 0.0774564623 0.010335327
## Npc2 0.08581197 0.002578323 -0.0534091808 0.0414969809 0.031483106
## Cd9 0.08562786 -0.047567501 -0.0031681715 -0.0200681472 -0.005125874
## Ank 0.08536142 -0.078449472 0.0191939352 0.0607812243 -0.025127000
## Csf1 0.08531621 -0.030680608 0.0296871408 -0.0236623880 -0.055435412
## Lgals3bp 0.08396246 0.069102467 -0.0468030777 0.0482506858 -0.008417322
## Ccl3 0.08213531 -0.022668987 0.0516302312 -0.0533060478 -0.065083854
## Itgax 0.08206594 -0.052744229 -0.0157853815 0.0025968625 -0.014005518
## Hif1a 0.08068022 -0.044666760 0.0177756171 0.0384092967 -0.045904385
## Eef1a1 0.08055212 -0.006837531 -0.0870921245 0.0077676585 0.054921588
## Ch25h 0.08044329 0.006519790 0.0685956613 -0.0335148396 -0.050724240
## Lilrb4a 0.07924947 -0.009810840 0.0485417523 -0.1272634887 -0.058516403
## H2-D1 0.07916361 0.093661223 -0.1244507098 -0.0456695562 0.069070390
## Ctsl 0.07899560 -0.008789674 -0.0503094611 0.0549571938 0.002892127
## Aplp2 0.07885196 -0.051408165 0.0210250705 -0.0045263253 -0.039743192
## Cox6a2 0.07773751 -0.024552437 -0.0150668438 0.0492204722 0.007773934
## Timp2 0.07754955 -0.021722874 -0.0612060248 0.0313058125 0.013932664
## Spp1 0.07708269 -0.013145039 0.0087505959 -0.0791512053 -0.011659033
## Axl 0.07636212 0.010533011 -0.0245902011 0.0003342218 -0.005580632
## Syngr1 0.07569752 -0.052629108 0.0007126634 0.0835071580 0.005345428
## Gapdh 0.07566385 0.000574007 0.0455580367 0.0347961106 0.015915273
## Pld3 0.07553428 -0.037776831 -0.0096437567 -0.0032568377 -0.017028082
## Fau 0.07479575 0.015691137 -0.1412978100 0.0044090514 0.103813309
## Crlf2 0.07335061 -0.028623250 -0.0398285034 0.0228477374 0.019167524
## PC_6 PC_7 PC_8
## Apoe 0.0982195466 -0.0983424473 -0.008502093
## Cst7 0.0290311737 -0.0099327053 -0.002512828
## Fth1 -0.0297440453 -0.0051944329 0.047743260
## Fabp5 0.0310095754 -0.0216505138 0.072161948
## Ctsb -0.0224101180 -0.0964747600 -0.105253709
## Cd63 -0.0242579948 -0.0271609402 -0.058514049
## Lyz2 0.0540286346 -0.0302003608 -0.032909971
## Ctsd -0.0381446250 -0.1027168315 -0.083474584
## Ctsz -0.0297080229 -0.0130895378 -0.091642101
## Cd52 -0.0358904233 0.0140737293 -0.019318791
## Igf1 0.0557677251 -0.0004352220 0.086835198
## Lpl 0.0002024007 0.0422521666 0.060760529
## Tyrobp -0.0747749449 0.0071451541 0.027927325
## Ftl1 -0.0910036900 -0.0989819743 0.024889730
## B2m -0.0098065539 -0.0077260605 -0.118524401
## Mif -0.0799994811 0.0130145225 0.012739368
## Gnas -0.0612871806 -0.0118103754 -0.029611396
## Npc2 -0.0293213849 -0.0266011510 -0.026496263
## Cd9 -0.0462955169 -0.0418240993 -0.074897457
## Ank 0.0490732929 0.0654246650 0.004755194
## Csf1 0.0448873163 0.0398570863 -0.012255144
## Lgals3bp -0.0096465214 -0.0769078085 -0.103942311
## Ccl3 -0.0278761076 -0.0002478853 -0.055699267
## Itgax 0.0814978539 0.0501108502 0.050126671
## Hif1a 0.0292531506 -0.0112293694 -0.056199937
## Eef1a1 -0.0029653472 -0.0230429748 0.004986999
## Ch25h -0.0296625436 0.0078588754 0.024366861
## Lilrb4a 0.0068344690 -0.0458841080 0.042066438
## H2-D1 0.0474573253 -0.0031320050 -0.111664276
## Ctsl -0.0243827936 0.0233000414 -0.086850952
## Aplp2 0.0661102779 -0.0003045849 0.005799693
## Cox6a2 0.0236124750 0.0596410625 0.026435133
## Timp2 -0.0130179956 -0.0894959304 -0.032941522
## Spp1 -0.0041292661 -0.0105701894 0.156801036
## Axl 0.0335174446 -0.0126276303 -0.019725643
## Syngr1 -0.0616502242 -0.0423401940 -0.012947192
## Gapdh -0.1017024202 0.0255953539 0.003827144
## Pld3 0.0039357945 -0.0613072216 -0.004684004
## Fau -0.0318015558 -0.0656638937 0.027016024
## Crlf2 0.0437455924 0.0284043632 -0.004665509(GEX.seur@reductions$pca@feature.loadings %>% as.data.frame)[,1:8] %>% arrange(PC_1 ) %>% head(40)## PC_1 PC_2 PC_3 PC_4 PC_5
## Crybb1 -0.07125275 -0.0174163744 0.061637045 0.0622269759 -0.043559430
## Ecscr -0.07005891 -0.0191041466 0.064442549 0.0267013693 -0.037679233
## Hpgd -0.06221909 -0.0597653126 0.062318226 0.0457007202 -0.031627713
## Slc2a5 -0.05589064 -0.0419630764 0.057743705 0.0668063712 -0.040937057
## Lrba -0.05361008 -0.0325767967 0.057948191 0.0345975740 -0.043381799
## Camk2n1 -0.04435957 -0.0098696682 0.045819352 0.0169471813 -0.026221481
## Filip1l -0.04377899 -0.0011387692 0.040880296 0.0095577183 -0.035658785
## Stab1 -0.04289914 -0.0222728250 0.056996894 0.0110011530 -0.047525520
## Slc40a1 -0.04203156 -0.0318016407 0.047780477 0.0316343685 -0.047249201
## Il7r -0.03997281 -0.0196073318 0.041092944 0.0206781712 -0.027762018
## Sox4 -0.03973813 -0.0472777076 0.055569593 0.0256207494 -0.041588757
## Lst1 -0.03859512 0.0260621751 -0.023167824 -0.0087605639 0.019426573
## Ddit4 -0.03662515 -0.0278450020 0.043430091 0.0233451253 -0.033957462
## Bank1 -0.03551595 -0.0221843210 0.049591048 0.0188714562 -0.038604348
## Rtn1 -0.03501590 -0.0174238003 0.032253465 0.0184276814 -0.019850249
## Tent5a -0.03351556 0.0125236395 0.011491486 0.0212649218 -0.044140608
## Fam102b -0.03293535 -0.0346810405 0.073080570 0.0619742028 -0.039531649
## Itga9 -0.03226772 -0.0170318533 0.036563965 0.0142177953 -0.034350696
## Serpinf1 -0.03162115 -0.0138954220 0.033631504 0.0078619090 -0.003371705
## Btg2 -0.03103233 -0.0123542271 0.027191907 -0.0147693919 -0.033752866
## Fcrls -0.03086449 -0.0482267162 0.088309504 0.0591203313 -0.076031969
## Lifr -0.02856426 -0.0092802571 0.033081122 0.0131790831 -0.028471524
## Gp9 -0.02829243 -0.0176643324 0.033464946 0.0225743866 -0.023541054
## Plxna4 -0.02802836 -0.0132848810 0.032468358 0.0125896755 -0.028106879
## Klf7 -0.02795906 -0.0077095681 0.034270729 -0.0050733047 -0.020162666
## Upk1b -0.02596451 -0.0283531600 0.046169394 0.0259925490 -0.028292079
## Cdk5r1 -0.02557628 -0.0146448972 0.037416482 0.0238729647 -0.024323067
## Nav3 -0.02448396 0.0223964283 0.040412767 0.0297930532 -0.036329002
## Gbp7 -0.02304218 0.0550766107 0.024410607 0.0230135826 -0.033562917
## Inpp4b -0.02303367 -0.0210410558 0.030079445 0.0186680503 -0.033392330
## Nfia -0.02283852 -0.0156165744 0.034132616 0.0205366355 -0.023408068
## Klk8 -0.02155732 -0.0093827933 0.007603048 0.0009380316 0.017973687
## Khdrbs3 -0.02155671 -0.0301695094 0.044366234 0.0350050297 -0.023831469
## Cask -0.02138948 -0.0174797001 0.031248999 0.0187915466 -0.031402407
## Arid4a -0.01845172 0.0001199639 0.019577780 0.0071954503 -0.021459080
## Hlf -0.01843268 -0.0117750565 0.022267571 0.0133176358 -0.014207626
## Fry -0.01833700 -0.0149468799 0.025258792 0.0253735495 -0.023778229
## Ttc28 -0.01809531 -0.0130445248 0.022546883 0.0124340781 -0.021437365
## Pde4b -0.01661339 -0.0032311142 0.016503162 -0.0064767670 -0.022405595
## Cdkn1c -0.01586221 -0.0136016631 0.007934058 0.0071646694 -0.007426902
## PC_6 PC_7 PC_8
## Crybb1 -0.0633415123 -3.077838e-02 0.0227968064
## Ecscr -0.0589302921 -4.044294e-02 0.0095210819
## Hpgd -0.0225862198 1.823019e-02 0.0466518379
## Slc2a5 -0.0246106177 2.629830e-02 0.0119073416
## Lrba -0.0278189644 -2.636712e-02 0.0176445691
## Camk2n1 -0.0329895618 -4.933705e-03 -0.0056216252
## Filip1l 0.0107368652 1.123482e-02 0.0095499267
## Stab1 -0.0449203732 -6.037662e-02 -0.0063307953
## Slc40a1 0.0007149646 -4.294400e-02 0.0021210651
## Il7r -0.0084401180 -1.127658e-02 0.0387445405
## Sox4 -0.0101545132 -2.138098e-02 0.0093627483
## Lst1 -0.0287745399 -2.165201e-02 -0.0225897213
## Ddit4 -0.0176411774 -1.390657e-02 0.0351905738
## Bank1 -0.0105288307 -2.027320e-02 0.0211278451
## Rtn1 -0.0179670528 7.098435e-03 0.0239164333
## Tent5a 0.0204204752 -3.857167e-02 0.0222459453
## Fam102b -0.0392025699 7.066474e-03 -0.0257295658
## Itga9 -0.0073982446 1.401138e-03 0.0169477878
## Serpinf1 -0.0184724875 6.733560e-03 0.0312529673
## Btg2 0.0287961850 -5.731496e-03 -0.0107138225
## Fcrls -0.0751583021 -9.902891e-02 0.0048186416
## Lifr 0.0077847703 3.519046e-02 0.0040133212
## Gp9 -0.0248514289 6.246959e-03 0.0294115728
## Plxna4 0.0025864000 -2.539216e-03 -0.0011822520
## Klf7 0.0002998945 1.644622e-02 0.0115026108
## Upk1b -0.0150634911 -8.489431e-03 0.0370942552
## Cdk5r1 -0.0099464639 -1.941281e-05 -0.0059808086
## Nav3 0.0200091867 8.065609e-02 -0.0596898203
## Gbp7 0.0227850095 5.635619e-02 0.0326802626
## Inpp4b 0.0105538683 -1.359776e-02 -0.0292245987
## Nfia 0.0079608316 7.632060e-03 -0.0083432447
## Klk8 -0.0257461335 4.992017e-02 0.0139343465
## Khdrbs3 -0.0120958931 -7.258875e-03 0.0334096196
## Cask -0.0120205747 -1.443649e-02 -0.0108235762
## Arid4a 0.0117583299 -1.232380e-02 -0.0054193193
## Hlf -0.0045868716 -5.792114e-03 0.0193860962
## Fry 0.0197553567 -6.929299e-03 0.0056989181
## Ttc28 0.0128794040 -1.540599e-02 -0.0131753956
## Pde4b -0.0009610003 -1.690763e-02 0.0009702224
## Cdkn1c -0.0107060483 -8.448315e-03 0.0159273319(GEX.seur@reductions$pca@feature.loadings %>% as.data.frame)[,1:8] %>% arrange(desc(PC_2) ) %>% head(40)## PC_1 PC_2 PC_3 PC_4 PC_5
## Ccl12 0.0162794739 0.16742224 -0.0040039504 0.013682355 -0.01507818
## Iigp1 0.0204528910 0.16032868 0.0069663107 0.042628181 -0.05802883
## Ccl2 0.0282896761 0.14263652 0.0520332667 0.002765441 -0.03542026
## Irf7 0.0318651314 0.12331869 0.0049404540 0.077901340 -0.08043553
## Fcgr4 0.0169605197 0.12304679 -0.0195139131 -0.003780755 0.02654228
## C4b 0.0150408778 0.11948059 -0.0480126604 -0.026857729 0.04227467
## Zbp1 0.0347682252 0.11744287 0.0054780670 0.061420669 -0.03491002
## Slfn5 0.0408002900 0.11666886 -0.0113764017 0.073375386 -0.11333964
## Fgl2 0.0266356713 0.11666022 -0.0343098776 0.042119985 -0.01244219
## Rsad2 0.0274427009 0.11661984 0.0155112740 0.074064878 -0.12201823
## Stat1 0.0278988065 0.11350502 -0.0108542966 0.056127469 -0.04931813
## Slfn2 0.0143732799 0.11234312 0.0015865877 0.028124852 -0.04970319
## Gbp2 0.0157322592 0.10708124 0.0144809798 0.020345508 -0.03229534
## H2-Q7 0.0383605171 0.10690828 -0.0677319693 -0.043531962 0.05196830
## Oasl2 0.0399187660 0.10638983 -0.0117524532 0.084516299 -0.08164191
## Oasl1 0.0209166294 0.10600626 0.0121561997 0.064980765 -0.09052601
## H2-K1 0.0683856966 0.10587091 -0.0932349214 -0.032098872 0.04722767
## Cd72 0.0553751986 0.10390699 0.0318429984 -0.062023031 0.02083902
## Bst2 0.0632629709 0.10366641 -0.0177913281 0.069007138 -0.01206454
## Cxcl10 0.0139911607 0.10344971 0.0380972757 0.034365485 -0.07675646
## Rtp4 0.0206657146 0.10173586 -0.0102176914 0.081300333 -0.06916225
## Tspo 0.0542517766 0.09975253 -0.0198969688 0.021404738 0.04125853
## Usp18 0.0267438188 0.09954353 -0.0008885168 0.063381971 -0.09173681
## Ly6a 0.0022563580 0.09798450 -0.0015057321 0.047378693 -0.02565861
## Phf11b 0.0302872845 0.09659267 0.0144178101 0.055511374 -0.05015406
## H2-D1 0.0791636121 0.09366122 -0.1244507098 -0.045669556 0.06907039
## Rnf213 0.0201541702 0.09237321 0.0040935776 0.053486251 -0.06641061
## Parp14 0.0113874444 0.09098651 -0.0020314401 0.060541096 -0.08355942
## Ms4a4c 0.0146097350 0.08601387 -0.0198232618 0.029051633 -0.03635740
## Tor3a 0.0162542058 0.08566886 -0.0023851148 0.023747697 -0.05574633
## Clec2d 0.0058176406 0.08560988 -0.0248405399 0.010669634 -0.03421866
## Trim30a 0.0173357849 0.08496637 0.0031088112 0.068530556 -0.06852049
## Sp100 0.0264298579 0.08314675 -0.0080804610 0.051753760 -0.04999209
## Naaa -0.0004988087 0.08226191 0.0053919752 0.002839857 0.02936179
## Xaf1 0.0240426349 0.08112075 -0.0036932287 0.063203771 -0.05618673
## Phf11d 0.0201153190 0.08104767 0.0052302414 0.060242881 -0.06134279
## H2-Q6 0.0212337590 0.08095222 -0.0311469889 -0.033554192 0.01989777
## Eif2ak2 0.0186915608 0.07960739 0.0065528684 0.055990260 -0.05175924
## Tap1 0.0172520039 0.07951389 0.0039316491 0.016044144 -0.01639107
## Irgm1 0.0097698549 0.07851849 0.0073718131 0.043003242 -0.05761390
## PC_6 PC_7 PC_8
## Ccl12 -0.015831877 0.1064469618 -0.059674320
## Iigp1 0.054274132 0.0523973061 0.099886455
## Ccl2 -0.034584955 0.0754511340 0.004256264
## Irf7 -0.022213058 -0.0623809968 0.041941975
## Fcgr4 -0.008489590 0.1157575343 -0.042743339
## C4b 0.011481987 0.0819919017 -0.072772535
## Zbp1 -0.022081982 -0.0049046570 0.042728270
## Slfn5 0.017816070 -0.0593081461 0.044080833
## Fgl2 0.035680180 0.0590292317 -0.017019890
## Rsad2 0.019488398 -0.1017206572 0.084326537
## Stat1 0.038221939 0.0010588611 0.010306586
## Slfn2 0.015884081 -0.0016120457 0.010574805
## Gbp2 0.018988903 0.0433765500 0.056597300
## H2-Q7 0.027068752 0.0045475046 -0.080500529
## Oasl2 0.020745337 -0.0591439789 0.025343858
## Oasl1 0.007262754 -0.0504890082 0.074773879
## H2-K1 0.023720777 0.0006597087 -0.127019760
## Cd72 -0.076568371 0.0593437795 0.010709725
## Bst2 -0.059694670 -0.0107658271 -0.026332219
## Cxcl10 0.025554260 -0.0393979703 0.067557948
## Rtp4 -0.001607410 -0.0467874242 0.018100120
## Tspo -0.045782592 0.0707880481 0.026033615
## Usp18 0.033466785 -0.0550425870 0.063094419
## Ly6a -0.006760161 0.0312121665 0.004691053
## Phf11b -0.021101275 -0.0220672471 0.047032331
## H2-D1 0.047457325 -0.0031320050 -0.111664276
## Rnf213 0.004368772 -0.0364419249 0.011778933
## Parp14 0.057329511 -0.0386082384 0.007518271
## Ms4a4c 0.016448487 -0.0134554225 0.044999694
## Tor3a 0.014061135 -0.0220619574 0.010343744
## Clec2d 0.041454300 -0.0067255208 0.019840436
## Trim30a 0.022439635 -0.0174939682 -0.005168355
## Sp100 0.016628435 -0.0129979743 0.003116756
## Naaa -0.006941461 0.1673120028 -0.023255224
## Xaf1 0.009203421 -0.0445425418 0.012291032
## Phf11d 0.011046491 -0.0244776065 0.052817020
## H2-Q6 0.028884681 0.0061324743 -0.066705533
## Eif2ak2 0.023056263 -0.0184201745 -0.004917068
## Tap1 0.023572100 -0.0003693182 0.002300167
## Irgm1 0.020699127 -0.0079206145 0.037388861(GEX.seur@reductions$pca@feature.loadings %>% as.data.frame)[,1:8] %>% arrange(PC_2) %>% head(40)## PC_1 PC_2 PC_3 PC_4 PC_5
## Trem2 0.072573562 -0.09476902 -0.0535656314 0.0822638219 -0.0006088877
## Ctsd 0.105748592 -0.08916149 -0.0219451625 0.0817485281 -0.0304836564
## Ank 0.085361424 -0.07844947 0.0191939352 0.0607812243 -0.0251270004
## Tyrobp 0.095997007 -0.07465886 -0.0572709372 0.0964621518 0.0407269962
## Igf1 0.100204212 -0.07084668 0.0085575795 -0.0066821365 -0.0396757934
## Ramp1 0.066821945 -0.06859341 -0.0009106848 0.0945544402 0.0039528464
## Serpine2 0.061276155 -0.06241428 0.0177811277 0.1212244665 -0.0423523666
## Pmp22 0.017925858 -0.06227385 0.0530114400 0.0212314553 -0.0432768851
## Ccl6 0.065401480 -0.06169190 0.0239999655 0.0392075675 0.0015770281
## Hpgd -0.062219085 -0.05976531 0.0623182264 0.0457007202 -0.0316277127
## Mamdc2 0.052140973 -0.05818772 -0.0110836210 0.0370293964 -0.0228383625
## Dkk2 0.050364620 -0.05746608 -0.0044434033 0.0207150685 -0.0291975958
## Baiap2l2 0.068953673 -0.05720752 0.0049583914 0.0810130203 -0.0227623637
## Hexa 0.055474587 -0.05665447 -0.0189513582 0.0891006239 -0.0082415496
## Ang 0.005129050 -0.05642911 0.0132529246 0.0641455992 -0.0120134010
## Cd68 0.064510398 -0.05557507 0.0251678736 0.0203259220 -0.0256276383
## Fabp5 0.110164577 -0.05279967 -0.0047849246 -0.0171977019 -0.0214767070
## Itgax 0.082065945 -0.05274423 -0.0157853815 0.0025968625 -0.0140055181
## Gpnmb 0.063765242 -0.05271795 -0.0055521445 -0.0302818188 -0.0397367580
## Syngr1 0.075697525 -0.05262911 0.0007126634 0.0835071580 0.0053454277
## Aplp2 0.078851963 -0.05140816 0.0210250705 -0.0045263253 -0.0397431921
## Cadm1 0.051739090 -0.05115093 0.0175340291 0.0409213443 -0.0248693470
## Scd2 0.044808079 -0.04901294 0.0372454009 0.0547175707 -0.0246417478
## Fcrls -0.030864494 -0.04822672 0.0883095035 0.0591203313 -0.0760319691
## Cst7 0.122385071 -0.04808590 -0.0358791868 0.0679274316 0.0070094875
## Cd9 0.085627865 -0.04756750 -0.0031681715 -0.0200681472 -0.0051258738
## Creg1 0.058861859 -0.04751559 -0.0206403713 0.0404787633 0.0107762549
## Sox4 -0.039738127 -0.04727771 0.0555695928 0.0256207494 -0.0415887571
## Arap2 0.037856508 -0.04709568 0.0153970842 0.0351295235 -0.0307207836
## St8sia6 0.044404271 -0.04559247 -0.0095322533 0.0230325811 0.0002929502
## Slc12a2 0.010848179 -0.04544818 0.0200535102 0.0503381735 -0.0407252130
## Crip1 0.064726232 -0.04471265 -0.0111398519 0.0007439775 0.0100403079
## Hif1a 0.080680221 -0.04466676 0.0177756171 0.0384092967 -0.0459043849
## Gpr183 -0.004487438 -0.04395056 0.0650220009 -0.0319637950 -0.0332159172
## Asb10 0.035129675 -0.04362704 0.0049945669 0.0343793328 -0.0272787187
## Slc2a5 -0.055890640 -0.04196308 0.0577437054 0.0668063712 -0.0409370572
## Lgi2 0.042221336 -0.04194324 0.0019697169 0.0607018242 -0.0165784235
## Lpl 0.099151712 -0.04118905 0.0528122116 -0.0752158158 -0.0273184804
## Arsb 0.001852044 -0.04114062 0.0551706173 0.0831666320 -0.0288928962
## Ctsa 0.054584378 -0.03983035 0.0066414219 0.0357924821 -0.0355123625
## PC_6 PC_7 PC_8
## Trem2 0.0161269840 -0.0030856035 -3.261265e-02
## Ctsd -0.0381446250 -0.1027168315 -8.347458e-02
## Ank 0.0490732929 0.0654246650 4.755194e-03
## Tyrobp -0.0747749449 0.0071451541 2.792733e-02
## Igf1 0.0557677251 -0.0004352220 8.683520e-02
## Ramp1 0.0269497500 0.0269577581 1.337887e-02
## Serpine2 0.0077746157 -0.0234802722 -8.183341e-02
## Pmp22 -0.0037317397 -0.0205957802 -5.736306e-02
## Ccl6 -0.0138745719 0.0710656468 2.586989e-02
## Hpgd -0.0225862198 0.0182301944 4.665184e-02
## Mamdc2 0.0879639496 0.0490573202 5.848202e-02
## Dkk2 0.0997016520 0.0563821374 8.811760e-02
## Baiap2l2 0.0342400715 0.0304473257 -1.387087e-02
## Hexa -0.0274126984 -0.0403642148 -4.493049e-02
## Ang -0.0127478073 -0.0021032115 1.636403e-02
## Cd68 -0.0412620952 -0.0370501503 -2.277190e-02
## Fabp5 0.0310095754 -0.0216505138 7.216195e-02
## Itgax 0.0814978539 0.0501108502 5.012667e-02
## Gpnmb 0.0959186423 -0.0234230321 1.314807e-01
## Syngr1 -0.0616502242 -0.0423401940 -1.294719e-02
## Aplp2 0.0661102779 -0.0003045849 5.799693e-03
## Cadm1 0.0365632596 0.0049759424 -4.812054e-02
## Scd2 -0.0042461769 0.0138118908 -1.427429e-02
## Fcrls -0.0751583021 -0.0990289054 4.818642e-03
## Cst7 0.0290311737 -0.0099327053 -2.512828e-03
## Cd9 -0.0462955169 -0.0418240993 -7.489746e-02
## Creg1 -0.0149536374 -0.0249466879 -1.488515e-02
## Sox4 -0.0101545132 -0.0213809844 9.362748e-03
## Arap2 0.0508635483 0.0204222622 1.934064e-02
## St8sia6 0.0752622436 0.0647453018 7.530945e-05
## Slc12a2 0.0067656431 -0.0374812828 -8.024335e-02
## Crip1 0.0329516834 0.0189413202 9.111362e-02
## Hif1a 0.0292531506 -0.0112293694 -5.619994e-02
## Gpr183 -0.0023405489 0.0387170331 2.606133e-02
## Asb10 0.0492501521 0.0329847028 9.269871e-03
## Slc2a5 -0.0246106177 0.0262983042 1.190734e-02
## Lgi2 0.0344698563 0.0591095321 -2.163076e-02
## Lpl 0.0002024007 0.0422521666 6.076053e-02
## Arsb -0.0068382948 -0.0012097709 -1.369025e-02
## Ctsa -0.0263860297 -0.0812089662 -7.233065e-02VlnPlot(GEX.seur, features = c("C4b","Fcgr4","Tspo","Naaa","Spp1","Spp1-EGFP"), ncol = 2, group.by = "cnt", pt.size = 0,cols = color.cnt) &
geom_jitter(alpha=0.15, shape=16, width = 0.2, size = 0.02)&
geom_boxplot(outlier.size = 0, fill="white", width=0.15, size=0.1, alpha=0.55) &
stat_summary(fun=mean, geom="point", shape=18, size=1.2, color="black", alpha=0.55)
decide PCs to use
ElbowPlot(GEX.seur,ndims = 50)
PCs <- 1:18
GEX.seur <- FindNeighbors(GEX.seur, dims = PCs, k.param = 20)## Computing nearest neighbor graph## Computing SNNGEX.seur <- FindClusters(GEX.seur, method = 'igraph' ,resolution = 1.2)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 21021
## Number of edges: 557343
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7466
## Number of communities: 15
## Elapsed time: 3 secondsRun UMAP/tSNE
GEX.seur <- RunTSNE(GEX.seur, dims=PCs, complexity = 100)
GEX.seur <- RunUMAP(GEX.seur, dims=PCs, n.neighbors = 20, seed.use = 287)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 17:55:01 UMAP embedding parameters a = 0.9922 b = 1.112## 17:55:01 Read 21021 rows and found 18 numeric columns## 17:55:01 Using Annoy for neighbor search, n_neighbors = 20## 17:55:01 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 17:55:04 Writing NN index file to temp file C:\Users\Shaorui\AppData\Local\Temp\Rtmp08wKfR\file4fe42ef5a37
## 17:55:04 Searching Annoy index using 1 thread, search_k = 2000
## 17:55:09 Annoy recall = 100%
## 17:55:10 Commencing smooth kNN distance calibration using 1 thread
## 17:55:11 Initializing from normalized Laplacian + noise
## 17:55:12 Commencing optimization for 200 epochs, with 617434 positive edges
## 17:55:33 Optimization finished#saveRDS(GEX.seur,"GEX0829.seur.pure_test.rds")DimPlot(GEX.seur, reduction = "tsne", label = T) + DimPlot(GEX.seur, reduction = "umap", label = T)
FeaturePlot(GEX.seur, reduction = "umap", features = c("nFeature_RNA","nCount_RNA","percent.mt","percent.rb"))
DimPlot(GEX.seur, reduction = "umap", label = T, group.by = "preAnno") +
DimPlot(GEX.seur, reduction = "umap", label = F, group.by = "cnt", cols = color.cnt)
pp.new.1a <- DimPlot(GEX.seur, reduction = "umap", label = T, group.by = "seurat_clusters") +
DimPlot(GEX.seur, reduction = "umap", label = F, group.by = "cnt", cols = color.cnt)
pp.new.1a
pp.new.1c <- DimPlot(GEX.seur, reduction = "umap", group.by = "seurat_clusters", split.by = "cnt", #cols = color.cnt,
ncol = 3)
pp.new.1c 
pp.new.1d <- DimPlot(GEX.seur, reduction = "umap", group.by = "cnt", split.by = "cnt", cols = color.cnt, ncol = 3)
pp.new.1d 
GEX.seur$sort_clusters <- factor(as.character(GEX.seur$seurat_clusters),
levels = c(0,1,2,
12,11,
3,7,9,14,
6,10,8,13,
4,5))VlnPlot(GEX.seur, features = c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.rb"),
ncol = 2,
pt.size = 0.05, group.by = "sort_clusters")
VlnPlot(GEX.seur, features = c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.rb"),
ncol = 2,
pt.size = 0, group.by = "sort_clusters")
markers.mig <- c("Ptprc",#"Cd3d","Cd3e","Cd19",
"Cd74","Lyz2","Ccl4",
"Aif1","P2ry12","C1qa","Spp1",
"Top2a","Pcna","Mki67","Mcm6",
"Cx3cr1","Il4ra","Il13ra1","Spp1-EGFP",
"Fabp5","Hmox1","Ms4a7","Cenpa")FeaturePlot(GEX.seur,
features = markers.mig,
ncol = 4)
DotPlot(GEX.seur, features = c("Cx3cr1","Spp1","Aif1", # Aif1: Iba1
"Fcer1g","Il4ra","Il13ra1"),
group.by = "sort_clusters")
DotPlot(GEX.seur, features = markers.mig, group.by = "sort_clusters",
cols = c("midnightblue","darkorange1")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, vjust = 1))#+ scale_y_discrete(limits=rev)
cowplot::plot_grid(
pheatmap::pheatmap(table(FB.info=GEX.seur$cnt,
clusters=GEX.seur$sort_clusters),
main = "Cell Count",
gaps_row = c(3,6),
#gaps_col = c(12,13,14),
cluster_rows = F, cluster_cols = F, display_numbers = T, number_format = "%.0f", legend = F, silent = T)$gtable,
pheatmap::pheatmap(100*rowRatio(table(FB.info=GEX.seur$cnt,
clusters=GEX.seur$sort_clusters)),
main = "Cell Ratio",
gaps_row = c(3,6),
#gaps_col = c(12,13,14),
cluster_rows = F, cluster_cols = F, display_numbers = T, number_format = "%.1f", legend = F, silent =T)$gtable,
ncol = 1)
all markers
# find markers for every cluster compared to all remaining cells, report only the positive ones
Idents(GEX.seur) <- "sort_clusters"
#GEX.markers.pre <- FindAllMarkers(GEX.seur, only.pos = TRUE, min.pct = 0.05,
# test.use = "MAST",
# #test.use = "wilcox",
# logfc.threshold = 0.25)
GEX.markers.pre <- read.table("sc10x_LYN.marker.pure0829.csv", header = TRUE, sep = ",")
GEX.markers.pre %>% group_by(cluster) %>% top_n(n = 8, wt = avg_log2FC)## # A tibble: 120 x 7
## # Groups: cluster [15]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 0 1.06 0.998 0.91 0 0 P2ry12
## 2 0 1.01 0.691 0.353 0 0 Crybb1
## 3 0 0.973 0.819 0.539 0 0 Cd164
## 4 0 0.895 0.936 0.745 0 0 Maf
## 5 0 0.880 0.987 0.848 0 0 Fcrls
## 6 5.28e-309 0.875 0.665 0.347 9.29e-305 0 Ecscr
## 7 7.70e-249 0.858 0.262 0.061 1.35e-244 0 Ddit4
## 8 1.02e-241 0.849 0.421 0.172 1.79e-237 0 Slc40a1
## 9 0 0.755 0.993 0.912 0 1 P2ry12
## 10 8.14e-285 0.607 0.981 0.925 1.43e-280 1 Gpr34
## # ... with 110 more rowsGEX.markers.pre$cluster <- factor(as.character(GEX.markers.pre$cluster),
levels = levels(GEX.seur$sort_clusters))
markers.pre_t32 <- (GEX.markers.pre %>% group_by(cluster) %>%
filter(pct.1>0.1) %>%
top_n(n = 32, wt = avg_log2FC) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj))$gene
markers.pre_t48 <- (GEX.markers.pre %>% group_by(cluster) %>%
filter(pct.1>0.05 & gene %in% grep("Rps|Rpl|mt-",GEX.markers.pre$gene,invert = T,value = T)) %>%
top_n(n = 48, wt = avg_log2FC) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj))$gene
markers.pre_t60 <- (GEX.markers.pre %>% group_by(cluster) %>%
filter(pct.1>0.01 & gene %in% grep("Rps|Rpl|mt-",GEX.markers.pre$gene,invert = T,value = T)) %>%
top_n(n = 60, wt = avg_log2FC) %>%
filter(p_val_adj < 0.01) %>%
ungroup() %>%
arrange(desc(avg_log2FC*pct.1),gene) %>%
distinct(gene, .keep_all = TRUE) %>%
arrange(cluster,p_val_adj))$geneDotPlot(GEX.seur, features = rev(markers.pre_t60[1:64])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(GEX.seur, features = rev(markers.pre_t60[65:128])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(GEX.seur, features = rev(markers.pre_t60[129:192])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(GEX.seur, features = rev(markers.pre_t60[193:256])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(GEX.seur, features = rev(markers.pre_t60[257:320])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(GEX.seur, features = rev(markers.pre_t60[321:384])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(GEX.seur, features = rev(markers.pre_t60[385:448])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
DotPlot(GEX.seur, features = rev(markers.pre_t60[449:508])) + coord_flip() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, size = 9.6))
signature score
#### 10x data, calculate signature score
#
## The code below is from Adam Hamber
## 2D scoring by Itay
get_controls <- function(counts, gene.list, verbose=F, control.genes.per.gene=10)
{
# Itay: "Such scores are inevitably correlated with cell complexity so to avoid
# that I subtract a "control" score which is generated by averaging over a control
# gene set. Control gene sets are chosen to contain 100 times more genes than the
# real gene set (analogous to averaging over 100 control sets of similar size) and
# to have the same distribution of population/bulk - based expression levels as the
# real gene set, such that they are expected to have the same number of "zeros" and
# to eliminate the correlation with complexity."
# ---------------------------------------------------------------------------------
# Going to find control points by finding the closest genes in terms of expression level and % of the time we observe it
if(verbose){cat(sprintf("Finding %s background genes based on similarity to given gene set [%s genes] \n",
control.genes.per.gene*length(gene.list), length(gene.list)))}
cat("Summarizing data \n")
summary = data.frame(gene=row.names(counts), mean.expr = Matrix::rowMeans(counts), fract.zero = Matrix::rowMeans(counts==0), stringsAsFactors = F)
#summary = data.frame(gene=row.names(counts), mean.expr = apply(counts,1,mean), fract.zero = apply(counts==0,1,mean), stringsAsFactors = F)
summary$mean.expr.s = scale(summary$mean.expr)
summary$fract.zero.s = scale(summary$fract.zero)
actual.genes = summary[summary$gene %in% gene.list,]
background.genes = summary[!summary$gene %in% gene.list,]
#find the 10 closest genes to each cell cycle marker gene and add them to the lists of control genes
get_closest_genes <- function(i)
{
background.genes$dist = sqrt((background.genes$mean.expr.s - actual.genes$mean.expr.s[i])^2 +
(background.genes$fract.zero.s - actual.genes$fract.zero.s[i])^2)
ordered = background.genes$gene[order(background.genes$dist)]
ordered = ordered[!ordered %in% controls] # don't take genes that already appear in the list
closest = head(ordered, n=control.genes.per.gene)
return(closest)
}
controls = c();
for (i in 1:length(gene.list)){
#info(sprintf("Finding %s control genes for %s", control.genes.per.gene, gene.list[i]))
closest = get_closest_genes(i)
#info(sprintf("Found %s: ", length(closest)))
controls = unique(c(controls, closest))
}
if(verbose){cat(sprintf("Control gene selection complete. %s genes found. \n", length(controls)))}
#print(controls)
return(controls)
}
## Define calculate function
calculate_signature_score <- function(count_matrix, gene_list){
control_gene <- get_controls(counts = count_matrix,
gene.list = gene_list)
signature_score <- colMeans(count_matrix[gene_list, ], na.rm = TRUE) -
colMeans(count_matrix[control_gene, ], na.rm = TRUE)
return(signature_score)
}
add_geneset_score <- function(obj, geneset, setname){
score <- calculate_signature_score(as.data.frame(obj@assays[['RNA']]@data),
geneset)
obj <- AddMetaData(obj,
score,
setname)
return(obj)
}DAM
DAM.sig <- read.csv("I:/Shared_win/projects/20230811_10x_LYN/analysis_plus_exogene/figures1002/new/ranking_of_DAM_indicator_genes.csv")
colnames(DAM.sig)[1] <- "Ranking"
DAM.sig[1:8,]## Ranking Gene Frequence Total.score
## 1 1 Spp1 11 35.98221
## 2 2 Apoe 11 31.44704
## 3 3 Ifitm3 9 20.82901
## 4 4 Ifi27l2a 9 20.44047
## 5 5 Lyz2 11 20.22463
## 6 6 Cst7 9 19.48372
## 7 7 Cd74 7 18.24720
## 8 8 Lgals3 7 18.24180DAM.list <- list(top50=DAM.sig$Gene[1:50],
top100=DAM.sig$Gene[1:100],
top250=DAM.sig$Gene[1:250],
top500=DAM.sig$Gene[1:500])DAM.list## $top50
## [1] "Spp1" "Apoe" "Ifitm3" "Ifi27l2a" "Lyz2" "Cst7"
## [7] "Cd74" "Lgals3" "Lpl" "Cd52" "Ccl5" "Ccl12"
## [13] "Lgals3bp" "Cd63" "H2-Ab1" "Fn1" "H2-Aa" "Fxyd5"
## [19] "Ccl4" "Cybb" "Bst2" "Cstb" "H2-Eb1" "Fth1"
## [25] "Vim" "Tspo" "Ctsb" "Ccl3" "Axl" "Anxa5"
## [31] "Isg15" "Lgals1" "Ccl2" "Ifi204" "Igf1" "Ch25h"
## [37] "Mif" "Cxcl10" "Plin2" "Fabp5" "Il1b" "Crip1"
## [43] "Slfn2" "B2m" "Irf7" "Cd72" "Capg" "Ms4a6c"
## [49] "Ifit3" "Apoc1"
##
## $top100
## [1] "Spp1" "Apoe" "Ifitm3" "Ifi27l2a" "Lyz2" "Cst7"
## [7] "Cd74" "Lgals3" "Lpl" "Cd52" "Ccl5" "Ccl12"
## [13] "Lgals3bp" "Cd63" "H2-Ab1" "Fn1" "H2-Aa" "Fxyd5"
## [19] "Ccl4" "Cybb" "Bst2" "Cstb" "H2-Eb1" "Fth1"
## [25] "Vim" "Tspo" "Ctsb" "Ccl3" "Axl" "Anxa5"
## [31] "Isg15" "Lgals1" "Ccl2" "Ifi204" "Igf1" "Ch25h"
## [37] "Mif" "Cxcl10" "Plin2" "Fabp5" "Il1b" "Crip1"
## [43] "Slfn2" "B2m" "Irf7" "Cd72" "Capg" "Ms4a6c"
## [49] "Ifit3" "Apoc1" "Fcgr4" "Il1rn" "Wfdc17" "Cxcl2"
## [55] "Cxcl16" "Prdx1" "Rtp4" "H2-D1" "Pkm" "Stat1"
## [61] "Anxa2" "Gpnmb" "Zbp1" "Ftl1" "Ldha" "Npc2"
## [67] "Ly6a" "Oasl2" "Gapdh" "Prdx5" "Gbp2" "Grn"
## [73] "Ifi207" "Ifitm2" "Tlr2" "Txn1" "Phf11b" "Ctsz"
## [79] "Pianp" "Cd36" "Itgax" "Fgl2" "Ccl6" "Iigp1"
## [85] "Ccl7" "H2-K1" "Pld3" "Tpi1" "Akr1a1" "Usp18"
## [91] "Rab7b" "Tmsb10" "Cd44" "Aldoa" "Hcar2" "Acod1"
## [97] "Cd300lb" "Ctsc" "Gpr65" "H2-Q7"
##
## $top250
## [1] "Spp1" "Apoe" "Ifitm3" "Ifi27l2a" "Lyz2" "Cst7"
## [7] "Cd74" "Lgals3" "Lpl" "Cd52" "Ccl5" "Ccl12"
## [13] "Lgals3bp" "Cd63" "H2-Ab1" "Fn1" "H2-Aa" "Fxyd5"
## [19] "Ccl4" "Cybb" "Bst2" "Cstb" "H2-Eb1" "Fth1"
## [25] "Vim" "Tspo" "Ctsb" "Ccl3" "Axl" "Anxa5"
## [31] "Isg15" "Lgals1" "Ccl2" "Ifi204" "Igf1" "Ch25h"
## [37] "Mif" "Cxcl10" "Plin2" "Fabp5" "Il1b" "Crip1"
## [43] "Slfn2" "B2m" "Irf7" "Cd72" "Capg" "Ms4a6c"
## [49] "Ifit3" "Apoc1" "Fcgr4" "Il1rn" "Wfdc17" "Cxcl2"
## [55] "Cxcl16" "Prdx1" "Rtp4" "H2-D1" "Pkm" "Stat1"
## [61] "Anxa2" "Gpnmb" "Zbp1" "Ftl1" "Ldha" "Npc2"
## [67] "Ly6a" "Oasl2" "Gapdh" "Prdx5" "Gbp2" "Grn"
## [73] "Ifi207" "Ifitm2" "Tlr2" "Txn1" "Phf11b" "Ctsz"
## [79] "Pianp" "Cd36" "Itgax" "Fgl2" "Ccl6" "Iigp1"
## [85] "Ccl7" "H2-K1" "Pld3" "Tpi1" "Akr1a1" "Usp18"
## [91] "Rab7b" "Tmsb10" "Cd44" "Aldoa" "Hcar2" "Acod1"
## [97] "Cd300lb" "Ctsc" "Gpr65" "H2-Q7" "Cdkn1a" "Psat1"
## [103] "Trim30a" "Cxcl14" "Srgn" "Mmp12" "Bcl2a1b" "Tap1"
## [109] "AB124611" "Xaf1" "Ly6e" "Psme1" "Ctsl" "Sp100"
## [115] "Cxcr4" "Psmb8" "AA467197" "Pgk1" "Emp3" "Csf1"
## [121] "Mcemp1" "Gusb" "Pim1" "Ctse" "Cox4i1" "Il12b"
## [127] "Msrb1" "Npm1" "Alcam" "Psme2" "Apoc2" "Bhlhe40"
## [133] "Stmn1" "Gm4951" "Pla2g7" "Plaur" "Tor3a" "Hspe1"
## [139] "Tpt1" "Ndufa1" "Flt1" "Tgfbi" "Cox6c" "Irgm1"
## [145] "Ifit2" "Uba52" "Igf2r" "Bola2" "Ank" "Tyrobp"
## [151] "Tpm4" "Ass1" "Ms4a4c" "Ifit1" "Ybx1" "Sod2"
## [157] "Cox8a" "Fam20c" "Oas1a" "Arg1" "Ms4a7" "Smpdl3a"
## [163] "Sh3pxd2b" "Fau" "Gnas" "Phf11d" "Ehd1" "Saa3"
## [169] "Cox5a" "Atox1" "Id3" "Lrpap1" "Olr1" "Sh3bgrl3"
## [175] "Sash1" "Hint1" "Il2rg" "Ctsd" "Postn" "H2-T23"
## [181] "Ucp2" "Sdc3" "Uqcrq" "Cox6a1" "Cd300lf" "Syngr1"
## [187] "Timp2" "Atp5e" "Id2" "S100a6" "Cd14" "Tubb6"
## [193] "Anp32b" "Fcgr2b" "Cd83" "Psmb9" "Bcl2a1a" "Aprt"
## [199] "Mfsd12" "Psap" "Cox6b1" "Lilr4b" "Plac8" "Glrx"
## [205] "Scimp" "Rilpl2" "Psmb6" "Gm11808" "Chmp4b" "Actr3b"
## [211] "Ly86" "Fundc2" "Ifi211" "Hif1a" "Serf2" "Dram1"
## [217] "Parp14" "Ptgs2" "Cxcl9" "Myo5a" "Gabarap" "Cyp4f18"
## [223] "Shisa5" "Lilrb4a" "Cpd" "Iqgap1" "Slfn5" "Tnfaip2"
## [229] "Nme1" "Cotl1" "Tagln2" "Mt1" "Mpeg1" "C3"
## [235] "Ube2l6" "Clic4" "Naaa" "Gas7" "Sdcbp" "Nampt"
## [241] "Ell2" "Samhd1" "Rtcb" "Eef1g" "Hmgb2" "Gng5"
## [247] "Nfil3" "Adora1" "Tpd52" "Ptger4"
##
## $top500
## [1] "Spp1" "Apoe" "Ifitm3" "Ifi27l2a"
## [5] "Lyz2" "Cst7" "Cd74" "Lgals3"
## [9] "Lpl" "Cd52" "Ccl5" "Ccl12"
## [13] "Lgals3bp" "Cd63" "H2-Ab1" "Fn1"
## [17] "H2-Aa" "Fxyd5" "Ccl4" "Cybb"
## [21] "Bst2" "Cstb" "H2-Eb1" "Fth1"
## [25] "Vim" "Tspo" "Ctsb" "Ccl3"
## [29] "Axl" "Anxa5" "Isg15" "Lgals1"
## [33] "Ccl2" "Ifi204" "Igf1" "Ch25h"
## [37] "Mif" "Cxcl10" "Plin2" "Fabp5"
## [41] "Il1b" "Crip1" "Slfn2" "B2m"
## [45] "Irf7" "Cd72" "Capg" "Ms4a6c"
## [49] "Ifit3" "Apoc1" "Fcgr4" "Il1rn"
## [53] "Wfdc17" "Cxcl2" "Cxcl16" "Prdx1"
## [57] "Rtp4" "H2-D1" "Pkm" "Stat1"
## [61] "Anxa2" "Gpnmb" "Zbp1" "Ftl1"
## [65] "Ldha" "Npc2" "Ly6a" "Oasl2"
## [69] "Gapdh" "Prdx5" "Gbp2" "Grn"
## [73] "Ifi207" "Ifitm2" "Tlr2" "Txn1"
## [77] "Phf11b" "Ctsz" "Pianp" "Cd36"
## [81] "Itgax" "Fgl2" "Ccl6" "Iigp1"
## [85] "Ccl7" "H2-K1" "Pld3" "Tpi1"
## [89] "Akr1a1" "Usp18" "Rab7b" "Tmsb10"
## [93] "Cd44" "Aldoa" "Hcar2" "Acod1"
## [97] "Cd300lb" "Ctsc" "Gpr65" "H2-Q7"
## [101] "Cdkn1a" "Psat1" "Trim30a" "Cxcl14"
## [105] "Srgn" "Mmp12" "Bcl2a1b" "Tap1"
## [109] "AB124611" "Xaf1" "Ly6e" "Psme1"
## [113] "Ctsl" "Sp100" "Cxcr4" "Psmb8"
## [117] "AA467197" "Pgk1" "Emp3" "Csf1"
## [121] "Mcemp1" "Gusb" "Pim1" "Ctse"
## [125] "Cox4i1" "Il12b" "Msrb1" "Npm1"
## [129] "Alcam" "Psme2" "Apoc2" "Bhlhe40"
## [133] "Stmn1" "Gm4951" "Pla2g7" "Plaur"
## [137] "Tor3a" "Hspe1" "Tpt1" "Ndufa1"
## [141] "Flt1" "Tgfbi" "Cox6c" "Irgm1"
## [145] "Ifit2" "Uba52" "Igf2r" "Bola2"
## [149] "Ank" "Tyrobp" "Tpm4" "Ass1"
## [153] "Ms4a4c" "Ifit1" "Ybx1" "Sod2"
## [157] "Cox8a" "Fam20c" "Oas1a" "Arg1"
## [161] "Ms4a7" "Smpdl3a" "Sh3pxd2b" "Fau"
## [165] "Gnas" "Phf11d" "Ehd1" "Saa3"
## [169] "Cox5a" "Atox1" "Id3" "Lrpap1"
## [173] "Olr1" "Sh3bgrl3" "Sash1" "Hint1"
## [177] "Il2rg" "Ctsd" "Postn" "H2-T23"
## [181] "Ucp2" "Sdc3" "Uqcrq" "Cox6a1"
## [185] "Cd300lf" "Syngr1" "Timp2" "Atp5e"
## [189] "Id2" "S100a6" "Cd14" "Tubb6"
## [193] "Anp32b" "Fcgr2b" "Cd83" "Psmb9"
## [197] "Bcl2a1a" "Aprt" "Mfsd12" "Psap"
## [201] "Cox6b1" "Lilr4b" "Plac8" "Glrx"
## [205] "Scimp" "Rilpl2" "Psmb6" "Gm11808"
## [209] "Chmp4b" "Actr3b" "Ly86" "Fundc2"
## [213] "Ifi211" "Hif1a" "Serf2" "Dram1"
## [217] "Parp14" "Ptgs2" "Cxcl9" "Myo5a"
## [221] "Gabarap" "Cyp4f18" "Shisa5" "Lilrb4a"
## [225] "Cpd" "Iqgap1" "Slfn5" "Tnfaip2"
## [229] "Nme1" "Cotl1" "Tagln2" "Mt1"
## [233] "Mpeg1" "C3" "Ube2l6" "Clic4"
## [237] "Naaa" "Gas7" "Sdcbp" "Nampt"
## [241] "Ell2" "Samhd1" "Rtcb" "Eef1g"
## [245] "Hmgb2" "Gng5" "Nfil3" "Adora1"
## [249] "Tpd52" "Ptger4" "Eif2ak2" "Areg"
## [253] "Rsad2" "Slc31a1" "Gm2000" "Cox7c"
## [257] "Tmem256" "Cox5b" "Cyba" "Il18bp"
## [261] "Selenow" "Myl6" "H2-DMa" "Rai14"
## [265] "Dab2" "Hmox1" "Tmsb4x" "Ndufa2"
## [269] "Cd68" "Ccdc86" "Atp6v1a" "Cox7a2"
## [273] "Calm1" "Uqcrh" "Socs2" "Gpi1"
## [277] "Ubl5" "Colec12" "Ifit3b" "Scpep1"
## [281] "S100a11" "F3" "Ms4a6d" "Hacd2"
## [285] "Chil3" "Tuba1c" "Rap2b" "Gp49a"
## [289] "Milr1" "Fcgr1" "Stat2" "Atp5j2"
## [293] "Uqcr10" "Ssr4" "Bcar3" "Gm49368"
## [297] "Tmem106a" "Tubb5" "Naca" "Hspa8"
## [301] "Atp5k" "Bag1" "Sec61b" "Gyg"
## [305] "Cox7b" "Ly6c2" "Top2a" "Aldh2"
## [309] "Dpp7" "Eif3k" "Cd48" "C4b"
## [313] "Pycard" "Atp5l" "Uqcr11" "Osm"
## [317] "Prelid1" "Rnf213" "Prdx4" "Arpc1b"
## [321] "Ndufv3" "Sp110" "Ndufa13" "Abca1"
## [325] "Gm1673" "Wdr89" "Sh3bgrl" "Smdt1"
## [329] "Gatm" "Gpr84" "Slamf8" "Ccrl2"
## [333] "Tomm7" "Gas8" "Ly6i" "Bcl2a1d"
## [337] "Esd" "Dhx58" "Ctsa" "Rxrg"
## [341] "Prdx6" "Ndufc1" "Polr2f" "Sdc4"
## [345] "Atp5j" "Litaf" "Atp6v0e" "Cspg4"
## [349] "Ranbp1" "Ifi35" "Fcer1g" "Calm3"
## [353] "Rhoc" "Pde4b" "Atp5g1" "Gpx3"
## [357] "Psmb1" "Gpx1" "Eef1a1" "Ly9"
## [361] "Igtp" "H2-Q6" "Herc6" "Cd9"
## [365] "Ran" "Hebp1" "Eno1" "Cdk18"
## [369] "Eef1b2" "Gbp7" "Glipr1" "Atp6v1f"
## [373] "H2-DMb1" "Btf3" "Slc25a3" "Brdt"
## [377] "Csf2ra" "Eif3f" "Hpse" "Gm14305"
## [381] "Gm14410" "H2-Q4" "Ndufb9" "Epsti1"
## [385] "Dnaaf3" "Pf4" "S100a4" "Atp5h"
## [389] "Apobec3" "Hsp90ab1" "Tnf" "Ctss"
## [393] "Gas6" "Gbp3" "Gng12" "Nceh1"
## [397] "Mgst1" "Cfl1" "Dtnbp1" "Slc2a6"
## [401] "Eif5a" "Atp5c1" "ptchd1" "Ptchd1"
## [405] "Psma7" "Lap3" "Rbm3" "Cycs"
## [409] "Cox6a2" "Abcg1" "Prr15" "Ahnak"
## [413] "Ndufb7" "Nrp1" "Lmnb1" "Ninj1"
## [417] "Mrpl33" "Arpc3" "Phlda1" "Acp5"
## [421] "Atp5g3" "C5ar1" "Arl5c" "Parp9"
## [425] "Ndufb8" "Txndc17" "Tbca" "Gm49339"
## [429] "Tma7" "Fblim1" "Eif3h" "Psme2b"
## [433] "Mrpl30" "Arpc2" "Ptma" "Rac2"
## [437] "Ctsh" "Dcstamp" "Clec4n" "Dbi"
## [441] "H2-T22" "Sem1" "Msr1" "Bax"
## [445] "S100a10" "Fabp3" "Snrpg" "Bcl2a1"
## [449] "Serpine2" "Snrpd2" "Cd180" "Pgam1"
## [453] "2310061I04Rik" "S100a1" "Serpinb6a" "Actg1"
## [457] "Uqcc2" "Tuba1b" "Neurl2" "Gcnt2"
## [461] "Smc2" "Atp5b" "Ifih1" "Nap1l1"
## [465] "Cd274" "Rgs1" "Parp12" "Serpine1"
## [469] "Nsa2" "Cebpb" "Csf2rb" "Cfb"
## [473] "Ndufa6" "Sdf2l1" "Anxa4" "Psma5"
## [477] "Nfkbiz" "Ctla2a" "Atp5o" "Ube2l3"
## [481] "Lipa" "Pfn1" "Ndufa7" "Ndufs6"
## [485] "Psmb10" "Mapkapk2" "Aif1" "Smc4"
## [489] "Itgb1bp1" "Nme2" "Pfdn5" "Rassf3"
## [493] "1810058I24Rik" "Pgls" "Clec4d" "Mrpl54"
## [497] "Tmem160" "Pomp" "Slc7a11" "F10"lapply(DAM.list, length)## $top50
## [1] 50
##
## $top100
## [1] 100
##
## $top250
## [1] 250
##
## $top500
## [1] 500GEX.seur <- add_geneset_score(GEX.seur, DAM.list$top50, "DAM.sig_top50")## Summarizing dataGEX.seur <- add_geneset_score(GEX.seur, DAM.list$top100, "DAM.sig_top100")## Summarizing dataGEX.seur <- add_geneset_score(GEX.seur, DAM.list$top250, "DAM.sig_top250")## Summarizing dataGEX.seur <- add_geneset_score(GEX.seur, DAM.list$top500, "DAM.sig_top500")## Summarizing datappnew.2b <- FeaturePlot(GEX.seur,features = c("DAM.sig_top50","DAM.sig_top100","DAM.sig_top250","DAM.sig_top500"), ncol = 4,
raster = T, pt.size = 3.5,
cols = c("lightgrey","red"))#,
#keep.scale = "all")
ppnew.2b
mapal <- colorRampPalette(RColorBrewer::brewer.pal(4,"Spectral"))(120)ppnew.2d <- FeaturePlot(GEX.seur,features = c("DAM.sig_top50","DAM.sig_top100","DAM.sig_top250","DAM.sig_top500"), ncol = 4,
raster = T, pt.size = 3.5,
keep.scale = "all") & scale_color_gradientn(colors = rev(mapal))
ppnew.2d
ppnew.2.v1 <- VlnPlot(GEX.seur, features = c("DAM.sig_top50","DAM.sig_top100","DAM.sig_top250","DAM.sig_top500"),
ncol = 2, pt.size = 0, raster = F) & geom_jitter(alpha=0.15, shape=16, width = 0.2, size = 0.02)
ppnew.2.v1
ppnew.2.v2 <- VlnPlot(GEX.seur, features = c("DAM.sig_top50","DAM.sig_top100","DAM.sig_top250","DAM.sig_top500"),
ncol = 2, pt.size = 0, raster = F) &
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) &
stat_summary(fun=mean, geom="point", shape=18, size=1.6, color="black", alpha=0.55)
ppnew.2.v2
ppnew.3.v1 <- VlnPlot(GEX.seur, features = c("DAM.sig_top50","DAM.sig_top100","DAM.sig_top250","DAM.sig_top500"),
group.by = "cnt", cols = color.cnt,
ncol = 2, pt.size = 0, raster = F) & geom_jitter(alpha=0.15, shape=16, width = 0.2, size = 0.02)
ppnew.3.v1
ppnew.3.v2 <- VlnPlot(GEX.seur, features = c("DAM.sig_top50","DAM.sig_top100","DAM.sig_top250","DAM.sig_top500"),
same.y.lims = F,
group.by = "cnt", cols = color.cnt,
ncol = 2, pt.size = 0, raster = F) &
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) &
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) &
ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("P05.CTR","P05.GFP"),
c("P28.CTR","P28.GFP")),
label.y = c(1,1.4),
size=3
)
ppnew.3.v2
ppnew.3.v3 <- VlnPlot(GEX.seur, features = c("DAM.sig_top50","DAM.sig_top100","DAM.sig_top250","DAM.sig_top500"),
same.y.lims = F,
group.by = "cnt", cols = color.cnt,
ncol = 2, pt.size = 0, raster = F) & ylim(c(-0.55,1.55)) &
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) &
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) &
ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("EAE.CTL","EAE.MIG"),
c("EAE.MIG","EAE.GFP"),
c("EAE.CTL","EAE.GFP"),
c("SIM.CTL","SIM.MIG"),
c("SIM.MIG","SIM.GFP"),
c("SIM.CTL","SIM.GFP"),
c("AD.CTL","AD.MIG"),
c("AD.MIG","AD.GFP"),
c("AD.CTL","AD.GFP")),
label.y = c(0.8,1.15,1.5,0.8,1.15,1.5,0.8,1.15,1.5),
size=1.5
)
ppnew.3.v3
module
module.path <- "I:/Shared_win/projects/20230211_microglia_module/analysis_new202310/result.new/modules.GO/"
module.list <- list(m1.blue=as.vector(unlist(read.table(paste0(module.path,"module01.blue.txt")))),
m2.purple=as.vector(unlist(read.table(paste0(module.path,"module02.purple.txt")))),
m3.darkorange=as.vector(unlist(read.table(paste0(module.path,"module03.darkorange.txt")))),
m4.yellow=as.vector(unlist(read.table(paste0(module.path,"module04.yellow.txt")))),
m5.yellowgreen=as.vector(unlist(read.table(paste0(module.path,"module05.yellowgreen.txt")))),
m6.green=as.vector(unlist(read.table(paste0(module.path,"module06.green.txt")))),
m7.darkgreen=as.vector(unlist(read.table(paste0(module.path,"module07.darkgreen.txt")))),
m8.turquoise=as.vector(unlist(read.table(paste0(module.path,"module08.turquoise.txt"))))
)lapply(module.list, length)## $m1.blue
## [1] 3070
##
## $m2.purple
## [1] 1286
##
## $m3.darkorange
## [1] 108
##
## $m4.yellow
## [1] 69
##
## $m5.yellowgreen
## [1] 45
##
## $m6.green
## [1] 375
##
## $m7.darkgreen
## [1] 700
##
## $m8.turquoise
## [1] 487names(module.list)## [1] "m1.blue" "m2.purple" "m3.darkorange" "m4.yellow"
## [5] "m5.yellowgreen" "m6.green" "m7.darkgreen" "m8.turquoise"for(nn in names(module.list)){
GEX.seur <- add_geneset_score(GEX.seur, module.list[[nn]], nn)
}## Summarizing data
## Summarizing data
## Summarizing data
## Summarizing data
## Summarizing data
## Summarizing data
## Summarizing data
## Summarizing datappnew.5b <- FeaturePlot(GEX.seur,features = c(names(module.list)), ncol = 4,
raster = T, pt.size = 3.5,
cols = c("lightgrey","red"))#,
#keep.scale = "all")
ppnew.5b
ppnew.5d <- FeaturePlot(GEX.seur,features = c(names(module.list)), ncol = 4,
raster = T, pt.size = 3.5,
keep.scale = "all") & scale_color_gradientn(colors = rev(mapal))
ppnew.5d
ppnew.6.v1 <- VlnPlot(GEX.seur, features = c(names(module.list)),
ncol = 2, pt.size = 0, raster = F) & geom_jitter(alpha=0.15, shape=16, width = 0.2, size = 0.02)
ppnew.6.v1
ppnew.6.v2 <- VlnPlot(GEX.seur, features = c(names(module.list)),
ncol = 2, pt.size = 0, raster = F) &
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) &
stat_summary(fun=mean, geom="point", shape=18, size=1.6, color="black", alpha=0.55)
ppnew.6.v2
ppnew.7.v1 <- VlnPlot(GEX.seur, features = c(names(module.list)),
group.by = "cnt", cols = color.cnt,
ncol = 2, pt.size = 0, raster = F) & geom_jitter(alpha=0.15, shape=16, width = 0.2, size = 0.02)
ppnew.7.v1
ppnew.7.v2 <- VlnPlot(GEX.seur, features = c(names(module.list)),
same.y.lims = F,
group.by = "cnt", cols = color.cnt,
ncol = 2, pt.size = 0, raster = F) &
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) &
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) &
ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("P05.CTR","P05.GFP"),
c("P28.CTR","P28.GFP")),
label.y = c(1,1.4),
size=3
)
ppnew.7.v2
ppnew.7.v3 <- VlnPlot(GEX.seur, features = c(names(module.list)),
same.y.lims = F,
group.by = "cnt", cols = color.cnt,
ncol = 2, pt.size = 0, raster = F) & ylim(c(-0.2,1.25)) &
geom_boxplot(outlier.size = 0, fill="white", width=0.2, size=0.1, alpha=0.55) &
stat_summary(fun=mean, geom="point", shape=18, size=3, color="black", alpha=0.55) &
ggpubr::stat_compare_means(aes(lable = ..p.signif..),
method = "wilcox.test",
comparisons = list(c("EAE.CTL","EAE.MIG"),
c("EAE.MIG","EAE.GFP"),
c("EAE.CTL","EAE.GFP"),
c("SIM.CTL","SIM.MIG"),
c("SIM.MIG","SIM.GFP"),
c("SIM.CTL","SIM.GFP"),
c("AD.CTL","AD.MIG"),
c("AD.MIG","AD.GFP"),
c("AD.CTL","AD.GFP"),
c("EAE.GFP","SIM.GFP"),
c("SIM.GFP","AD.GFP"),
c("EAE.GFP","AD.GFP")),
label.y = c(0.5,0.6,0.7,0.5,0.6,0.7,0.5,0.6,0.7,0.8,0.9,1.0),
size=2
)
ppnew.7.v3
final features
final.list <- list(a=c("H2-Aa","Cd74","H2-Ab1",
"H2-Eb1","Iigp1","Gpx3"),
b=c("Spp1","Fabp5","Tspo",
"Axl","Ly9"),
c=c("Ccl5","Ccl7","Ccl12",
"Cxcl10","Stat1"),
d=c("Trem2","Grn","Cd9",
"Tyrobp","Atp6ap1","Fam20c"),
e=c("Tmem119","Cx3cr1","Slc2a5",
"Sall1","Tgfbr1"),
f=c("Igf1","Gnas","Fdps",
"Dhcr7","Cyp51"))lapply(final.list, function(x){x %in% rownames(GEX.seur)})## $a
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
##
## $b
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $c
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $d
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
##
## $e
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $f
## [1] TRUE TRUE TRUE TRUE TRUElapply(final.list, function(x){
lapply(module.list, function(y){
x %in% y
})
})## $a
## $a$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $a$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $a$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $a$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $a$m5.yellowgreen
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
##
## $a$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $a$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $a$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
##
## $b
## $b$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $b$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $b$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $b$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $b$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $b$m6.green
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $b$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $b$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE
##
##
## $c
## $c$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $c$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $c$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $c$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $c$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $c$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $c$m7.darkgreen
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $c$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE
##
##
## $d
## $d$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $d$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $d$m3.darkorange
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
##
## $d$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $d$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $d$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $d$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $d$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
##
## $e
## $e$m1.blue
## [1] FALSE FALSE FALSE FALSE TRUE
##
## $e$m2.purple
## [1] TRUE TRUE TRUE TRUE FALSE
##
## $e$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $e$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $e$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $e$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $e$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $e$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE
##
##
## $f
## $f$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $f$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $f$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $f$m4.yellow
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $f$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $f$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $f$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $f$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSEfinal.list.rename <- list(m5.yellowgreen=c("H2-Aa","Cd74","H2-Ab1",
"H2-Eb1","Iigp1","Gpx3"),
m6.green=c("Spp1","Fabp5","Tspo",
"Axl","Ly9"),
m7.darkgreen=c("Ccl5","Ccl7","Ccl12",
"Cxcl10","Stat1"),
m3.darkorange=c("Trem2","Grn","Cd9",
"Tyrobp","Atp6ap1","Fam20c"),
m21.purpleblue=c("Tmem119","Cx3cr1","Slc2a5",
"Sall1","Tgfbr1"),
m4.yellow=c("Igf1","Gnas","Fdps",
"Dhcr7","Cyp51"))lapply(final.list.rename, function(x){
lapply(module.list, function(y){
x %in% y
})
})## $m5.yellowgreen
## $m5.yellowgreen$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m5.yellowgreen$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m5.yellowgreen$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m5.yellowgreen$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m5.yellowgreen$m5.yellowgreen
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
##
## $m5.yellowgreen$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m5.yellowgreen$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m5.yellowgreen$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
##
## $m6.green
## $m6.green$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m6.green$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m6.green$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m6.green$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m6.green$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m6.green$m6.green
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $m6.green$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m6.green$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE
##
##
## $m7.darkgreen
## $m7.darkgreen$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m7.darkgreen$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m7.darkgreen$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m7.darkgreen$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m7.darkgreen$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m7.darkgreen$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m7.darkgreen$m7.darkgreen
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $m7.darkgreen$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE
##
##
## $m3.darkorange
## $m3.darkorange$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m3.darkorange$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m3.darkorange$m3.darkorange
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
##
## $m3.darkorange$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m3.darkorange$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m3.darkorange$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m3.darkorange$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
## $m3.darkorange$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE FALSE
##
##
## $m21.purpleblue
## $m21.purpleblue$m1.blue
## [1] FALSE FALSE FALSE FALSE TRUE
##
## $m21.purpleblue$m2.purple
## [1] TRUE TRUE TRUE TRUE FALSE
##
## $m21.purpleblue$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m21.purpleblue$m4.yellow
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m21.purpleblue$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m21.purpleblue$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m21.purpleblue$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m21.purpleblue$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSE
##
##
## $m4.yellow
## $m4.yellow$m1.blue
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m4.yellow$m2.purple
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m4.yellow$m3.darkorange
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m4.yellow$m4.yellow
## [1] TRUE TRUE TRUE TRUE TRUE
##
## $m4.yellow$m5.yellowgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m4.yellow$m6.green
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m4.yellow$m7.darkgreen
## [1] FALSE FALSE FALSE FALSE FALSE
##
## $m4.yellow$m8.turquoise
## [1] FALSE FALSE FALSE FALSE FALSEfinal.list.rename## $m5.yellowgreen
## [1] "H2-Aa" "Cd74" "H2-Ab1" "H2-Eb1" "Iigp1" "Gpx3"
##
## $m6.green
## [1] "Spp1" "Fabp5" "Tspo" "Axl" "Ly9"
##
## $m7.darkgreen
## [1] "Ccl5" "Ccl7" "Ccl12" "Cxcl10" "Stat1"
##
## $m3.darkorange
## [1] "Trem2" "Grn" "Cd9" "Tyrobp" "Atp6ap1" "Fam20c"
##
## $m21.purpleblue
## [1] "Tmem119" "Cx3cr1" "Slc2a5" "Sall1" "Tgfbr1"
##
## $m4.yellow
## [1] "Igf1" "Gnas" "Fdps" "Dhcr7" "Cyp51"lapply(final.list.rename,function(x){
FeaturePlot(GEX.seur,features = c(x), ncol = 3,
raster = T, pt.size = 3.5,
cols = c("lightgrey","red"))
})## $m5.yellowgreen
##
## $m6.green
##
## $m7.darkgreen
##
## $m3.darkorange
##
## $m21.purpleblue
##
## $m4.yellow
lapply(final.list.rename,function(x){
FeaturePlot(GEX.seur,features = c(x), ncol = 3,
raster = T, pt.size = 3.5,
cols = c("lightgrey","red"))& scale_color_gradientn(colors = rev(mapal))
})## $m5.yellowgreen
##
## $m6.green
##
## $m7.darkgreen
##
## $m3.darkorange
##
## $m21.purpleblue
##
## $m4.yellow
#saveRDS(GEX.seur,"./GEX0829.seur.pure_sort.rds")forGEO
GEX.seur <- readRDS("./GEX0829.seur.pure_sort.rds")
GEX.seur## An object of class Seurat
## 17590 features across 21021 samples within 1 assay
## Active assay: RNA (17590 features, 1390 variable features)
## 3 dimensional reductions calculated: pca, tsne, umapGEX.seur@meta.data[,grep("snn|pANN",colnames(GEX.seur@meta.data),value = T)] <- NULL
head(GEX.seur@meta.data)## orig.ident nCount_RNA nFeature_RNA percent.mt percent.rb
## AAACCCAAGAAGTCCG-1 Spp1ND 1777 950 2.138436 8.610017
## AAACCCAAGAGAAGGT-1 Spp1ND 2811 1304 1.529705 12.877979
## AAACCCAAGCACTCAT-1 Spp1ND 3211 1426 3.083152 15.789474
## AAACCCAAGTCATCCA-1 Spp1ND 3553 1643 2.420490 11.004785
## AAACCCAAGTCGGCCT-1 Spp1ND 6412 1938 1.325639 17.358079
## AAACCCACAAGAGGTC-1 Spp1ND 3014 1054 2.687459 20.902455
## FB.info S.Score G2M.Score Phase seurat_clusters
## AAACCCAAGAAGTCCG-1 SIM.MIG -0.001773983 -0.00433029 G1 1
## AAACCCAAGAGAAGGT-1 AD.GFP.1 -0.034564958 0.01326095 G2M 4
## AAACCCAAGCACTCAT-1 AD.GFP.1 0.004795299 -0.01011902 S 9
## AAACCCAAGTCATCCA-1 SIM.CTL 0.003007456 -0.01007400 S 1
## AAACCCAAGTCGGCCT-1 EAE.GFP 0.076364443 -0.06412611 S 9
## AAACCCACAAGAGGTC-1 EAE.GFP -0.010117249 -0.01113632 G1 7
## cnt sort_clusters DoubletFinder0.05 DoubletFinder0.1
## AAACCCAAGAAGTCCG-1 SIM.MIG 1 Singlet Singlet
## AAACCCAAGAGAAGGT-1 AD.GFP 4 Singlet Singlet
## AAACCCAAGCACTCAT-1 AD.GFP 9 Singlet Singlet
## AAACCCAAGTCATCCA-1 SIM.CTL 1 Singlet Singlet
## AAACCCAAGTCGGCCT-1 EAE.GFP 9 Singlet Singlet
## AAACCCACAAGAGGTC-1 EAE.GFP 7 Singlet Singlet
## preAnno PC_1 PC_2 DAM.sig_top50
## AAACCCAAGAAGTCCG-1 Microglia -5.4471120 -1.41232903 -0.12886958
## AAACCCAAGAGAAGGT-1 Microglia 0.7139283 -2.41197770 0.05988822
## AAACCCAAGCACTCAT-1 Microglia 6.7730504 -1.69563381 0.29955511
## AAACCCAAGTCATCCA-1 Microglia -4.1035505 -0.76945994 -0.36419246
## AAACCCAAGTCGGCCT-1 Microglia 10.8851187 2.14596437 0.82758593
## AAACCCACAAGAGGTC-1 Microglia 2.9593327 0.05956462 0.29261936
## DAM.sig_top100 DAM.sig_top250 DAM.sig_top500 m1.blue
## AAACCCAAGAAGTCCG-1 -0.05322557 0.0006620787 0.0917550 0.10662676
## AAACCCAAGAGAAGGT-1 0.01828038 0.1322962948 0.2170636 0.11369759
## AAACCCAAGCACTCAT-1 0.28877250 0.2463909542 0.3972866 0.08340758
## AAACCCAAGTCATCCA-1 -0.24372908 -0.0888669115 0.1221884 0.15953562
## AAACCCAAGTCGGCCT-1 0.58889142 0.5116411395 0.5554119 0.06910936
## AAACCCACAAGAGGTC-1 0.22824103 0.2793449722 0.3333028 0.04203820
## m2.purple m3.darkorange m4.yellow m5.yellowgreen
## AAACCCAAGAAGTCCG-1 0.19167658 0.134384240 -0.06013164 -0.01712984
## AAACCCAAGAGAAGGT-1 0.17418624 0.113741642 0.10167319 0.02485532
## AAACCCAAGCACTCAT-1 0.11077992 0.197485327 0.21626831 -0.05288063
## AAACCCAAGTCATCCA-1 0.17844982 0.003841682 -0.03585387 -0.08453258
## AAACCCAAGTCGGCCT-1 0.07126132 0.240925300 0.08158639 0.02887132
## AAACCCACAAGAGGTC-1 0.11175430 0.242583580 0.07825132 0.18873265
## m6.green m7.darkgreen m8.turquoise
## AAACCCAAGAAGTCCG-1 0.3442975 -0.038220365 -0.03207072
## AAACCCAAGAGAAGGT-1 0.5728791 -0.031307089 -0.06703069
## AAACCCAAGCACTCAT-1 0.8033512 -0.016257571 0.07148222
## AAACCCAAGTCATCCA-1 0.4835077 -0.054728716 -0.01170676
## AAACCCAAGTCGGCCT-1 0.9344805 0.089015706 0.08991283
## AAACCCACAAGAGGTC-1 0.8320493 0.005748232 -0.03052330#saveRDS(GEX.seur,"I:/Shared_win/projects/202310_Spp1DAM/forGEO/seur_obj/Spp1GFP_Modelx3.final.seur_obj.rds")